Bridging the Gap: Enhancing the Evaluation & Interpretation of Epidemic Forecasts for Researchers & Policymakers in Resource-Constrained Settings

Background: The COVID-19 pandemic highlighted the complexities of crisis decision-making, where evidence generation, interpretation, and action occurred simultaneously. These processes unfolded under compressed timeframes, uncertainty, and politically charged environments. Epidemic forecasts became key tools for navigating uncertainty, yet their use varied widely. This study aimed to explore how forecasts were used, interpreted, and communicated in pandemic decision-making, and how knowledge translation could be improved. Methods: We conducted a global mixed-methods study, combining an online survey and semi-structured interviews with individuals involved in COVID-19 policy dialogues across 46 countries. Eligible participants had directly or indirectly contributed to pandemic policy decisions. Of 143 survey respondents, 105 (73.4%) reported involvement at national, subnational, or field levels. We conducted 13 interviews with individuals engaged in COVID-19 decision-making. Findings: Forecasts were used to address six types of policy questions, including epidemic spread, health system capacity, and intervention planning. Their perceived value depended on clarity, timeliness, and relevance. Preferences for metrics and formats varied: some valued forecasts for communicating uncertainty and exploring scenarios, especially in settings with strong modelling capacity. Others emphasized the role of expert briefings and locally adapted insights, especially where baseline data or technical capacity was limited. Across all settings, forecasts were most actionable when supporting binary decisions or scenario comparisons. Key barriers included poor data, late delivery, lack of contextual fit, and limited familiarity with modelling outputs. Interpretation: These findings highlight the need for modular, user-facing tools that accommodate varying decision contexts and capacities. Embedding modellers in response teams, co-developing decision-relevant metrics, and strengthening health data systems are critical. Future pandemic responses will benefit from technical improvements and stronger institutional mechanisms for knowledge translation and collaboration.